Data Science: Sculpturing the Future

Eniola Elizabeth Fase

Ever wondered how services like FedEx and Amazon strike at destination within time with the best possible routes, the bulk of information found in a single DNA sample is stored and can be accessed or utilized for certain research, or ever have a thought about how enormous amounts of data is revolving just around your fingers.

To quench the thirst for modern era problems we entered the world of Data Science.

What is Data Science?

Data science is the field of study that mixes domain expertise, computational and programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. So, briefly and sweet “Data science is that which governs the discipline of creating useful data”.

Historical aspects which commence the evolution of Data science

A varied amount of data is well understood by statisticians and can be presented in several ways but all the efforts are available in paper raw form in a non-interactive space eventually resulting in futile utilization.   

  •  Data means the introduction of computers with headway.
  • Statisticians can’t code their way out of a paper sheet.

And that’s why here is the Sexiest Job of the 21st Century according to Harvard the Data Scientist.

Given below is a pictorial presentation of perspective, skills, domain knowledge, and disciplines which adheres to the principle of a data scientist.

[By Calvin.Andrus (Own work) [CC BY-SA 3.0 (], via Wikimedia Commons]

Fundamentals of Data Science

  • Business/Domain
  • Mathematics (includes Data analysis and probability)
  • Computer science (software engineering/Data structure etc.)
  • Communication (written and lingual)

There are other skills and expertise that are highly desirable also, but these are the first four in our opinion. These are going to be mentioned because of the data scientist pillars for the remainder of this text.

In reality, people are often strong in one or two of those pillars, but usually not equally strong altogether four. If it is possible to satisfy a knowledge scientist that’s truly an expert altogether, then it’s a consolidate hit on head of the nail.

Based on these pillars, data scientist definition may be one that should be ready to leverage existing data sources and make new ones as required to extract meaningful information and actionable insights. A Data scientist does this through business sphere expertise, effective communication and results in interpretation, and optimum use of any and each one relevant statistical technique, programming languages, software abilities, and libraries, and data infrastructure. The details that data scientists reveal should be used to drive business decisions and take actions focused to understand commerce goals.

Data Science Venn Diagrams

One can find many various versions of the Data scientist Venn diagram to assist visualize these pillars (or variations) and their relationships with each other. A fantastic article was written by David Taylor on these Venn diagrams entitled, Battle of the Data Science Venn Diagrams. We highly recommend reading it.

Here is one of the preferred data scientist Venn diagrams created by Stephan Kolassa. One may notice that the first ellipses within the diagram are very almost like the pillars given above.

[Author: Stephan Kolassa]

The Data Science Process

  • Business Understanding – During this initiative, it’s an attempt to get a far better idea of what business needs should always be extracted from data. What questions should be asked to assist further the business and to assist the business to understand what sorts of actions it should take from the trends that the info shows? This might be an open lead, because the data scientist, asks questions on the info that are simple to see and find or it might be a series of questions from the client that they specifically want to understand.
  • Data Understanding – This is often getting a business idea of the info that simply has an understanding of what each part of the info means. This might involve deciding what data would be best needed and therefore the best ways to accumulate it. This also means checking out what each of the info points signifies in terms of the business.
  • Data Preparation – The info preparation a part of the method is where most of the time is going to be. Cleaning the info is often more of a kind than a science since one has got to understand if the initiator got the right data to proceed to an honest model and knowing the way to clean it correctly so it won’t corrupt the model.
  • Modeling – Here is where doing statistics and analyzing the info are available to make a model that most closely fits the info. Focus is needed to try several models to seek out one with the simplest fit. To try to do that, going back to how the info was prepared may often happen. There are more ways to wash missing data.
  • Evaluation – This part is where the test is conducted to ascertain if one has acquired an honest model or not before deploying or presenting. Because the diagram indicates, this is often also the part where the creator ensures the model answers the business questions you had at the start of this process. Perhaps it’s going to even uncover more questions that are more important.
  • Deployment – This is often where the creator shares findings of the data. This isn’t limited to having an application programming interface to call that uses the model. It could simply be documenting the findings in an email, a shared document, or presenting to a gaggle of executives. While it’s easy to speak technical together with colleagues, relaying what is needed to discover within the data to a sales team or the executives so that they can take action with it’s the key with this step.

Data Science Aims and Yields

To comprehend the need for these fundamentals of data science, one must initially understand the typical goals and services/products related to data science initiatives, and also the data science process itself. Following are the major yields of data science

  • Predicting and eliminating disease
  • Personalized healthcare recommendations
  • Optimizing shipping and Air routes in real-time
  • Approximation and accuracy in handling bulk data
  • Stamping out tax fraud
  • Automating digital ad placement
  • Scoring and ranking
  • Segmentation
  • Optimization

Data Scientist Pre-requisite and Education In-Depth

We’ve already seen the commerce domain and communication basics, which represent business acumen and top-notch communication skills. It’s very helpful therein data scientists typically need to present and communicate results to key shareholders, including executives.

So strong soft skills, particularly communication (written and verbal) and speechmaking ability are keys. Within the phase where results are communicated and delivered, the magic is within the data scientist’s ability to deliver the leads in a clear, compelling, and insightful way, while using appropriate language and jargon level for his or her audience.

For all of the opposite phases listed, data scientists must draw upon strong programming skills, also as knowledge about statistics, probabilities, and arithmetic to know the info, choose the right solution approach, implement the answer, and improve thereon also.

The single paramount thing to focus on is off-the-shelf data science platforms and APIs. One could also be shall think that these are often used relatively easily and thus not require significant expertise in certain fields, and thus do not require a robust, well-rounded data scientist.

Some of these include having the power to:

• Customize the approach and solution to the precise problem at hand to maximize results, including the power to write down new algorithms and/or significantly modify the prevailing ones, consistent with the use

• Access and query many various databases and data sources (RDBMS, NoSQL, NewSQL), also as integrate the info into an analytics-driven data source (e.g., OLAP, warehouse, data lake, etc.).

• Find and choose the optimal data sources and data features (variables), including creating new ones as required (feature engineering).

• Understand all statistical, programming, and library/package options available, and choose the simplest.

• Ensure data has high integrity (good data), quality (the right data), and is in optimal form and condition to ensure accurate, reliable, and statistically significant results.

• Select and implement the simplest tooling, algorithms, frameworks, languages, and technologies to maximize results and scale as required.

• Choose the right performance metrics and apply the acceptable techniques to maximize performance.

• Discover ways to leverage the info to realize business goals without guidance and/or deliverables being dictated from the highest down, i.e., the info scientist because of the idea person.

• Work cross-functionally, effectively, and together with all company departments and groups.

• Distinguish well from bad results and thus mitigate the potential risks and financial losses which will come from erroneous conclusions and subsequent decisions.

• Understand product (or service) customers and/or users and make ideas and solutions with them in mind.

Education-wise, there’s no single path to becoming a knowledge scientist. Many universities have created data science and analytics-specific programs, mostly at the master’s degree level. Some universities and other organizations also offer certification programs.

No matter what path is taken to find out, data scientists should have advanced quantitative knowledge and highly technical skills, primarily in statistics, mathematics, and computing.



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